Systems and methods for processing and displaying patient electrocardiograph data

ABSTRACT

A method is disclosed for displaying patient ECG data. The method includes receiving ECG data including an ECG waveform; receiving analyzed ECG data including arrhythmic events; generating an indicia of the detected arrhythmic event; and displaying the indicia of the detected arrhythmic event in relation to the ECG waveform at a position associated with a time of the detected arrhythmic event. A system for displaying patient ECG data is also disclosed.

RELATED APPLICATION(S)

This application is a continuation of U.S. patent application Ser. No.14/088,950, filed Nov. 25, 2013, which is a continuation of U.S. patentapplication Ser. No. 13/763,180, filed Feb. 8, 2013, now U.S. Pat. No.8,620,418 (issued Dec. 31, 2013), which claims priority to U.S.Provisional Patent Application No. 61/749,052 filed on Jan. 4, 2013, theentire disclosures of which are incorporated herein by reference.

TECHNICAL FIELD

Various embodiments of the present disclosure relate generally todisplaying patient health data. More specifically, particularembodiments of the present disclosure relate to systems and methods forobtaining and processing patient health data, such as electrocardiograph(ECG) data, and displaying the data to a healthcare professional on ahandheld device.

BACKGROUND

Remote monitoring of ambulatory patients enables doctors to detect ordiagnose heart problems, such as arrhythmias, that may produce onlytransient symptoms and, therefore, may not be evident when the patientsvisit the doctors' offices. Several forms of cardiac event monitors havebeen used.

A “Holter” monitor is worn by a patient and collects and stores data fora period of time, typically at least 24 hours, and in some cases up totwo weeks. After the data has been collected, the Holter monitor istypically brought or sent to a doctor's office, laboratory or the like,and the data is retrieved from the monitor and analyzed. Holter monitorsare relatively inexpensive, but they cannot be used for real-timeanalysis of patient data, because the data is analyzed hours, days orweeks after it has been collected.

More timely analysis of heart data is made possible by pre-symptom(looping memory) event monitors. Such a device collects and storespatient data in a “loop” memory device that constantly overwritespreviously stored data with newly collected data. The event monitor mayinclude a button, which the patient is instructed to actuate if thepatient feels ill or otherwise detects a heart-related anomaly. Inresponse, the event monitor continues to record data for a short periodof time and then stops recording, thereby retaining data for a timeperiod that spans the button actuation. The retained data may then besent via a modem and a telephone connection to a doctor's office or to alaboratory for analysis.

Mobile Cardiac Telemetry (MCT) refers to a technique that involvesnoninvasive ambulatory cardiac event monitors that are capable ofcontinuous measurements of heart rate and rhythm over several weeks. Forexample, some MCT devices include an automatic electrocardiograph (ECG)arrhythmia detector that couples to a cellular telephone device toimmediately transmit automatically detected abnormal ECG waveforms to aremote monitoring center, which can then alert a physician. Such devicesalso include a memory capable of storing ECG waveform data, which istransmitted to a cellular phone for analysis, and then to the remotemonitoring center whenever an event is detected by the smartphonealgorithms. Although data about automatically detected arrhythmias issent immediately to the remote monitoring center, without requiringpatient action, the computational resources and corresponding electricalpower (battery) required to perform the automatic ECG analysis in thedevice are significant.

Some MCT devices continuously send all collected ECG data to a remotemonitoring center for analysis. These MCT devices typically do notperform any ECG analysis of their own. Although no patient-initiatedaction is required, the large amount of data transmitted by the MCTwireless devices consumes more of the wireless bandwidth used to conveythe data. Furthermore, a large amount of computational resources isrequired at the remote monitoring center to analyze the continuousstream of received data, especially when many patients are monitored bya single data center.

To improve the collection, transmission and processing of physiologicaldata, InfoBionic of Lowell, Mass. has developed a novel system thatcollects high definition physiologic data, but sends a downsampledversion of it to a remote server for the first-pass processing. When theremote server detects an arrhythmia, it requests the high resolutiondata from the transceiver for a second-pass analysis. Embodiments ofthis system are disclosed in U.S. patent application Ser. No.13/446,490, filed on Apr. 13, 2012, the entirety of which is herebyincorporated herein by reference.

However, to date, regardless of how much ECG data is collected andanalyzed, and whether ECG data is analyzed on a local or remote device,the resulting ECG data is typically presented to physicians in long,printed reports. Such printed reports of ECG data are static, andtherefore do not include the latest ECG data obtained from a patientdevice, and are also not able to be manipulated by a reviewingphysician. Moreover, printed reports are tedious to review and difficultto understand, which makes physicians less interested in reviewing thosereports. As a result, review of printed reports of ECG data is sometimesdelayed and/or delegated to junior physicians. Thus, while the systemsand methods of the '490 application address certain challengesassociated with the collection and analysis of immense amounts of ECGdata, a need remains for improved systems and methods for reporting anddisplaying collected and processed ECG data for a plurality of patientsto healthcare professionals.

SUMMARY OF THE DISCLOSURE

A method is disclosed for displaying patient ECG data. The methodincludes receiving ECG data including an ECG waveform; receivinganalyzed ECG data to detect an arrhythmic event experienced by thepatient; generating an indicia of the detected arrhythmic event; anddisplaying the indicia of the detected arrhythmic event in relation tothe ECG waveform at a position associated with a time of the detectedarrhythmic event.

The indicia of the detected arrhythmic event includes an indication of adetected severity of the detected arrhythmic event or an indication of adetected recency of the detected arrhythmic event. The indicia of thedetected arrhythmic event has a size that increases based on a detectedseverity of the detected arrhythmic event, or a color or shape thatchanges based on a detected severity of the detected arrhythmic event.

The method further includes classifying the patient into one of aplurality of patient groups based on a detected severity or a detectedrecency of the detected arrhythmic event. The plurality of patientgroups include: a first group of patients that have experienced a recentarrhythmic event, a second group of patients that have not experienced arecent arrhythmic event and a third group of patients that havecompleted a prescribed monitoring period. The method further includesgenerating a group indicia associated with each of the plurality ofpatient groups; wherein a size of a group indicia associated with thefirst group of patients is bigger than a size of a group indiciaassociated with the second group of patients, or a color of a groupindicia associated with the first group of patients is brighter than acolor of a group indicia associated with the second group of patients.

The method further includes generating a group indicia associated witheach of the plurality of patient groups; and displaying the plurality ofgroup indicia, each group indicia including an identification of one ormore patients classified into the patient group of the group indicia. Acolor, shape, or size of each group indicia is changed based on a numberof patients classified in the group, or a number or a severity of one ormore detected arrhythmic events of patients classified in the group.

The method further includes generating a display of indicia of aplurality of patients, each indicia of each of the plurality of patientsincluding an ECG waveform and indicia of a detected arrhythmic eventassociated with each respective patient; and sorting the displayedindicia of the plurality of patients based on a classifying of each ofthe plurality of patients into one of the plurality of patient groups.The method further includes generating a display of indicia of aplurality of patients, each indicia of each of the plurality of patientsincluding an ECG waveform and indicia of a detected arrhythmic eventassociated with each respective patient; and sorting a sequence of thedisplayed indicia of the plurality of patients based on a number, arecency, or a severity of one or more detected arrhythmic events foreach patient.

The ECG data is received from a sensor associated with a patient. Themethod further includes receiving, from a physician, a request to viewthe ECG data; and transmitting, to the physician, one or more imagesthat displays the indicia of the detected arrhythmic event in relationto the ECG waveform at a position associated with a time of the detectedarrhythmic event.

The method further includes generating a group indicia associated witheach of the plurality of patient groups, each group indicia beingrepresentative of a planet or astronomical object in the universe, anddisplaying the plurality of group indicia, each group indicia includingan identification of one or more patients classified into the patientgroup of the group indicia.

A system is disclosed for displaying patient ECG data. The systemincludes a data storage device storing instructions for displayingpatient ECG data; and a processor configured to execute the instructionsto perform a method comprising: receiving ECG data including an ECGwaveform; receiving analyzed ECG data to detect an arrhythmic eventexperienced by the patient; generating an indicia of the detectedarrhythmic event; and displaying the indicia of the detected arrhythmicevent in relation to the ECG waveform at a position associated with atime of the detected arrhythmic event.

The indicia of the detected arrhythmic event includes an indication of adetected severity of the detected arrhythmic event or an indication of adetected recency of the detected arrhythmic event. The indicia of thedetected arrhythmic event has a size that increases based on a detectedseverity of the detected arrhythmic event, or a color or shape thatchanges based on a detected severity of the detected arrhythmic event.

The processor is further configured for: classifying the patient intoone of a plurality of patient groups based on a detected severity or adetected recency of the detected arrhythmic event. The plurality ofpatient groups include: a first group of patients that have experienceda recent arrhythmic event, a second group of patients that have notexperienced a recent arrhythmic event, and a third group of patientsthat have completed a prescribed monitoring period.

The processor is further configured for: generating a group indiciaassociated with each of the plurality of patient groups; wherein a sizeof a group indicia associated with the first group of patients is biggerthan a size of a group indicia associated with the second group ofpatients, or a color of a group indicia associated with the first groupof patients is brighter than a color of a group indicia associated withthe second group of patients.

The processor is further configured for: generating a group indiciaassociated with each of the plurality of patient groups; and displayingthe plurality of group indicia, each group indicia including anidentification of one or more patients classified into the patient groupof the group indicia. A color, shape, or size of each group indicia ischanged based on a number of patients classified in the group, or anumber or a severity of one or more detected arrhythmic events ofpatients classified in the group.

The processor is further configured for: generating a display of indiciaof a plurality of patients, each indicia of each of the plurality ofpatients including an ECG waveform and indicia of a detected arrhythmicevent associated with each respective patient; and sorting the displayedindicia of the plurality of patients based on a classifying of each ofthe plurality of patients into one of the plurality of patient groups.

The processor is further configured for: generating a display of indiciaof a plurality of patients, each indicia of each of the plurality ofpatients including an ECG waveform and indicia of a detected arrhythmicevent associated with each respective patient; and sorting a sequence ofthe displayed indicia of the plurality of patients based on a number, arecency, or a severity of one or more detected arrhythmic events foreach patient.

The ECG data is received from a sensor associated with a patient. Theprocessor is further configured for: receiving, from a physician, arequest to view the ECG data; and transmitting, to the physician, one ormore images that displays the indicia of the detected arrhythmic eventin relation to the ECG waveform at a position associated with a time ofthe detected arrhythmic event.

The processor is further configured for generating a group indiciaassociated with each of the plurality of patient groups, each groupindicia being representative of a planet or astronomical object in theuniverse, and displaying the plurality of group indicia, each groupindicia including an identification of one or more patients classifiedinto the patient group of the group indicia.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 is a schematic diagram of a system and environment forcollecting, processing, and displaying ECG data, according to anexemplary embodiment of the present disclosure.

FIG. 2 is a flow diagram of a method for collecting, processing, anddisplaying ECG data, according to an exemplary embodiment of the presentdisclosure.

FIG. 3 is a schematic diagram of a device, e.g., sensors, positioned ona patient torso for collecting patient ECG data, according to anexemplary embodiment of the present disclosure.

FIG. 4 is a schematic diagram of a portion of ECG data, reflected in ahypothetical ECG waveform of data collected by the system and methods ofFIGS. 1-3, according to an exemplary embodiment of the presentdisclosure.

FIG. 5 is a screenshot of a physician interface for reviewing patientECG data, according to an exemplary embodiment of the presentdisclosure.

FIG. 6 is another screenshot of a physician interface for reviewingpatient ECG data, according to an exemplary embodiment of the presentdisclosure.

FIG. 7 is another screenshot of a physician interface for reviewingpatient ECG data, according to an exemplary embodiment of the presentdisclosure.

FIG. 8 is another screenshot of a physician interface for reviewingpatient ECG data, according to an exemplary embodiment of the presentdisclosure.

FIG. 9 is another screenshot of a physician interface for reviewingpatient ECG data, according to an exemplary embodiment of the presentdisclosure.

FIG. 10 is another screenshot of a physician interface for reviewingpatient ECG data, according to an exemplary embodiment of the presentdisclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of thedisclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

In view of the challenges outlined above, systems and methods aredisclosed for remote physiologic monitoring of a body of a patient, inassociation with a remote server. In one embodiment, the system mayinclude a plurality of sensors and a transceiver assembly. Each sensorof the plurality of sensors may be configured to be coupled to the bodyof the patient to generate respective physiologic data about the body.The transceiver assembly may include a memory, a controller and awireless transceiver. The transceiver assembly may be communicativelycoupled to the plurality of sensors. The transceiver assembly may beconfigured to receive the physiologic data from the plurality ofsensors. The transceiver assembly may also be configured to store thereceived physiologic data in the memory.

In one embodiment, the transceiver and sensors may be configured tocollect patient ECG data according to the embodiments and methodsdescribed in the '490 application (described above and incorporatedherein by reference). In particular, the transceiver assembly may beconfigured to send a subset of the received physiologic data (referredto as “less detailed data”), via the wireless transceiver, to the remoteserver. The less detailed data sent to the remote server may becharacterized by a lower resolution than some “more detailed data”stored in the memory for a corresponding time period and/or a lowersampling rate than the more detailed data stored in the memory for acorresponding time period, and/or having been received from a differentset of the sensors than the more detailed data stored in the memory fora corresponding time period. The transceiver assembly may be configuredto fetch at least a portion of the more detailed physiologic data fromthe memory, in response to a signal from the remote server. In addition,in response to the signal from the remote server, the transceiverassembly is configured to send the fetched more detailed physiologicdata to the remote server. The remote server may be configured toreceive the less detailed physiologic data sent by the transceiverassembly and automatically analyze the received less detailedphysiologic data for an indication of a health-related anomaly. If thehealth-related anomaly is indicated, the remote server may be configuredto automatically send the signal to the transceiver assembly. Thehealth-related anomaly may be or include an arrhythmia. In oneembodiment, the wireless transceiver assembly may include a cellulartelephone coupled via a short-range wireless link to the wirelesstransceiver. The cellular telephone may be configured to store the moredetailed data in the memory, send the less detailed data to the remoteserver, responsive to the signal, fetch the at least the portion of themore detailed physiologic data from the memory, and send the fetchedmore detailed physiologic data to the remote server via a wirelesscarrier network. Although, the presently disclosed embodiments may beused with the “more detailed data” and “less detailed data” collectionscheme described above and in the '490 application, the presentlydisclosed embodiments may be used in relation to any remote arrhythmiadetection system, regardless of the quantity of ECG or arrhythmia datacollected.

Referring now to the enclosed figures, FIG. 1 is a schematic diagram ofa system and environment for collecting, processing, and displaying ECGdata, according to an exemplary embodiment of the present disclosure. Asshown in FIG. 1, the system and environment may include a plurality ofphysician devices 102 and patient devices 104 disposed in communicationwith an electronic network 100. Electronic network 100 may be theInternet, or any other combination of wired and/or wireless electronicnetworks.

In one embodiment, each of physician devices 102 may include a server,personal computer, tablet computer, mobile device, smartphone, and/orpersonal digital assistant (“PDA”) disposed in communication withelectronic network 100. For example, in one embodiment, each ofphysician devices 102 may be a touchscreen enabled device, such as anApple iPad, Samsung Galaxy, Amazon Kindle, Microsoft Surface, or anyother equivalent or similar device. Each of physician devices 102 mayhave a web browser or mobile browser installed for receiving anddisplaying content from web servers. Thus, each of physician devices 102may be configured to receive and display data that is received andprocessed from patient devices 104, over electronic network 100.

In one embodiment, each of patient devices 104 may include a combinationof physiologic sensors, a memory, a battery, and/or a transceiver, oneor more of which may be disposed within or in communication with amobile device, such as a smartphone, PDA, or other handheld or wearableelectronic device. In one embodiment, the physiologic sensors may bedisposed in short-range wireless, Bluetooth, radio-frequency (RFID),and/or near-field communications (NFC) communication with a mobiledevice carried or worn by the patient. Each of patient devices 104 maybe connected to electronic network 100 through a cellular network and/ora Wi-Fi network. Thus, each of patient devices 104 may be configured tocollect physiological data from a patient, and transmit collectedphysiological data over electronic network 100. Each of patient devices104 may also have a web browser or mobile browser installed forreceiving and displaying content from web servers.

As shown in FIG. 1, a plurality of server systems 106, a browser webserver 114, and/or a mobile web server 116 may also be disposed incommunication with electronic network 100. In one embodiment, serversystems 106 may be configured to receive physiological data from patientdevices 104 over electronic network 100. Any of the devices orfunctionality of server systems 106, browser web server 114, and/or amobile web server 116 may be combined together or separated, and may beoperated by a single administrative entity, or outsourced to one or moreother entities, such as a web hosting entity, web storage entity, and/orcloud computing service.

As shown in the embodiment of FIG. 1, server systems 106 may include aphysiological data analyzer 110, which may be configured to performhigh-sensitivity analysis and high specificity analysis on receivedphysiological data. Specifically, physiological data analyzer 110 may beconfigured to analyze received physiological data for detectingarrhythmic events, determine a severity of any detected arrhythmicevents, and/or perform any other analysis, classification, and/orsorting of detected arrhythmic events and/or patients having experiencedarrhythmic events, as will be described in more detail below.

Server systems 106 may also include one or more databases 108, wheredata analyzer 110 may be configured to store the received physiologicaldata. As described above with respect to the '490 application, serversystem 106 may be configured to receive and store either “less detaileddata” and/or “more detailed data,” or a portion thereof. Any receiveddata may be stored in the databases 108 in an encrypted form to increasesecurity of the data against unauthorized access.

Server systems 106 may also include a physician application program 112that allows a physician to control parameters of the system, such asthreshold values used by the data analyzer 110 in performinghigh-sensitivity and/or high-specificity analyses. The physicianapplication program 112 also displays data to the physician and allowsthe physician to select types of data to display, time periods of thedata to display, levels of data detail to display and other operatingparameters of the system. For example, the physician may select abeginning and ending time surrounding a suspected or verified arrhythmiafor display. In response to a query by the physician, the physicianapplication program 112 may fetch and display data from the databases108. If the requested data is not available in the databases 108, or ifthe requested data is not available in the database 108 at the level ofdetail requested by the physician, the physician application program 112may automatically communicate with the transceiver of a patient device104 to fetch the appropriate data in the appropriate amount of detail.

The physician application program 112 may implement appropriate securityprotocols, such as requiring the physician to enter logon credentials,so as to appropriately limit access to patient data and comply withregulations, such as the Health Insurance Portability and AccountabilityAct (HIPAA).

As shown in FIG. 1, server systems 106 may be disposed in communicationwith a browser web server 114 and/or a mobile web server 116. Each ofbrowser web server 114 and/or mobile web server 116 may be configured tointeract with physician devices 102, such as to accept user (physician,patient or administrator) inputs and generate appropriate displays tofacilitate user interaction with the physician application program 112.For example, browser web server 114 and/or mobile web server 116 may beconfigured to generate a window-metaphor based computer user interfaceon a screen of physician device(s) 102 or screen (not shown) coupled tothe remote server systems 106, or the browser web server 114 and/ormobile web server 116 may generate web pages that are rendered by abrowser or application of the physician devices 102. The physiciandevices 102 and the browser web server 114 and/or mobile web server 116may communicate with each other using an appropriate encrypted protocol,such as Hypertext Transfer Protocol Secure (HTTPS).

FIG. 2 is a flow diagram of a method 200 for collecting, processing, anddisplaying ECG data, e.g., using the exemplary system and devices ofFIG. 1, according to an exemplary embodiment of the present disclosure.As shown in FIG. 2, method 200 may initially include receiving ECG datafrom one or more patients (step 202). For example, server systems 106may receive ECG data from one or more patient devices 104, which maythen be stored in databases 108. In one embodiment, patient devices 104may include or may be disposed in communication with a plurality ofsensors.

FIG. 3 is a schematic diagram of a device, e.g., sensors, positioned ona patient torso for collecting patient ECG data, according to anexemplary embodiment of the present disclosure. Specifically, FIG. 3 isa schematic diagram illustrating one possible combination ofphysiological sensors 300, 303 and 309 and a possible placement of thesensors on a torso 312 of a patient. One of the sensors 309 may beattached at about the elevation of the diaphragm of the patient. Eachsensor 300-309 may be attached to the torso 312 using known gel pads orother conventional attachment techniques. Any combination of knownphysiological electrodes may be used for the sensors 300-309. Forexample, the sensors 300-309 may include any combination of SpO2sensors, blood pressure sensors, ECG electrodes, respiration sensors,movement and activity sensors, and the like. Movement or activity may besensed with appropriate accelerometers or gyroscopes, such as microelectro-mechanical system (MEMS) devices. The sensors 300-309 may beconnected via wires or optical cables 315 and 318 or via wireless links,such as Bluetooth links. Respiration data may be derived from ECGbaseline data, as is known to those of skill in the art. Optionally,other sensors, such as a patient weight measuring device, blood pressurecuff, etc., may be disconnectably coupled via wires, optical cables orwirelessly to a transceiver assembly of patient devices 104. Thus, asdiscussed above, patient devices 104 may be configured to collectphysiologic data, store the collected data in a memory, and send a fulldetail or less-detailed version of the data to the remote server systems106 for storage in databases 108.

Referring now back to FIG. 2, method 200 may then include processing thereceived ECG data (step 204). In one embodiment, processing the receivedECG data may include detecting arrhythmic events (step 214). Forexample, the ECG data may be processed by the data analyzer 110 toautomatically classify heartbeats using morphology and heartbeatinterval features, as described by Philip de Chazal, et al., in“Automatic Classification of Heartbeats Using ECG Morphology andHeartbeat Interval Features,” IEEE Transactions on BiomedicalEngineering, Vol. 51, No. 7, July, 2004, the contents of which arehereby incorporated by reference. In other words, collected data may beprocessed before a determination is made whether an anomaly has beendetected. As noted, arrhythmia may be suspected or verified (or both)using ECG data, non-ECG data, or a combination thereof. For example, anarrhythmia may be suspected or verified, based in whole or in part onrespiration rate. The respiration rate may be determined based on datafrom one or more accelerometers in the sensors attached to the torso ofthe patient, as shown for example in FIG. 3. Chest movements detected bythe accelerometers may be filtered, such as within expected frequenciesand amplitudes, to derive the respiration rate. For example, oneaccelerometer may be included in the sensor 309 (FIG. 3), which islocated adjacent the patient's diaphragm, and another accelerometer maybe include in the sensor 300 or 303. Relative motion between the twolocations on the torso 312 represented by the two accelerometers closelyrepresents diaphragm movement and, therefore, breathing.

In addition to detecting arrhythmic events, the processing of the ECGdata (of step 204) may also include generating an indicia of eachdetected arrhythmic event (step 216). For example, any type of indicia,such as an icon of a box, circle, planet, sphere, or any other shape maybe generated to represent a detected arrhythmic event, as will bedescribed in more detail below. In one embodiment, the generated indiciaof the detected arrhythmic event may include an indication of a detectedseverity of the detected arrhythmic event or an indication of a detectedrecency of the detected arrhythmic event. In yet another embodiment, thegenerated indicia of the detected arrhythmic event may have a size thatincreases based on a detected severity of the detected arrhythmic event,or a color or shape that changes based on a detected severity of thedetected arrhythmic event.

The processing of the ECG data (of step 204) may also includeassociating the generated indicia with patient ECG data (step 218). Forexample, indicia of a detected arrhythmic event may be displayed inrelation to the ECG waveform of a patient, at a position associated witha time of the detected arrhythmic event.

The processing of the ECG data (of step 204) may also includecategorizing patients based on the detected arrhythmic events (step220). For example, method 200 may include classifying each patient intoone of a plurality of patient groups based on a detected severity or adetected recency of the detected arrhythmic event. In one embodiment,the plurality of patient groups may include a first group of patientsthat have experienced a recent arrhythmic event, a second group ofpatients that have not experienced a recent arrhythmic event, and athird group of patients that have completed a prescribed monitoringperiod. In one embodiment, a size of a group indicia associated with thefirst group of patients may be bigger than a size of a group indiciaassociated with the second group of patients, or a color of a groupindicia associated with the first group of patients may be brighter thana color of a group indicia associated with the second group of patients.

The processing of the ECG data (of step 204) may also include sortingpatients based on the detected arrhythmic events (step 222). Forexample, method 200 may include generating a display of indicia of aplurality of patients, each indicia of each of the plurality of patientsincluding an ECG waveform and indicia of any detected arrhythmic eventassociated with each respective patient, and sorting the displayedindicia of the plurality of patients based on a classifying of each ofthe plurality of patients into one of the plurality of patient groups.In one embodiment, method 200 may include sorting a sequence of thedisplayed indicia of the plurality of patients based on a number, arecency, or a severity of one or more detected arrhythmic events foreach patient.

Method 200 may further include, either concurrently with orasynchronously from processing the ECG data, receiving a request for ECGdata from a physician (step 206). For example, a physician may use abrowser or other software installed on a physician device 102 togenerate a request for ECG data from browser web server 114, mobile webserver 116, and/or server systems 106. The physician may generate therequest by simply manipulating a user interface, such as touching a userelement associated with a patient for whom the physician desires toreview ECG data. Alternatively, the physician may request ECG data forall of the physician's patients, or all of the patients of thephysician's practice (e.g., the physician's patients and patients of thephysician's partners, nurse practitioners, residents, supervisingphysicians, etc.).

Method 200 may further include transmitting the processed ECG data to aphysician (step 208). For example, method 200 may include transmittingone or more images of ECG data and/or processed ECG data to thephysician device 102 over electronic network 100. Transmission of ECGdata may include displaying an ECG waveform for one or more patients tothe physician. Transmission of ECG data may also include displayingindicia of detected arrhythmic events, indicia of groups of patients,and/or indicia of groups of arrhythmic events. For example, method 200may include displaying to the physician a plurality of group indiciagenerated for each of a plurality of patient groups, where each groupindicia may include an identification of one or more patients classifiedinto the patient group of the group indicia.

Method 200 may also include receiving an input from a physician tomodify a display of ECG data (step 210). For example, method 200 mayinclude receiving an input from a physician based on the physician'smanipulation of a user element of a user interface of a physician device102. In one embodiment, the input may include a swiping, squeezing, orpinching a display of an ECG waveform associated with a patient. FIG. 4contains a hypothetical ECG waveform 400, representing detailed datacollected from the sensors of patient devices 104. In one embodiment,the collected data may have a relatively high sampling rate and arelatively high resolution. Alternatively or additionally, the collecteddata may be downsampled or have lower resolution. As shown in FIG. 4,the waveform 400 may include a portion 403, during which the waveform isanomalous, e.g., representing a detected arrhythmia.

Method 200 may then include modifying a display of ECG data based onreceived physician input (step 212). For example, a displayed waveform400 may be advanced through time, expanded to cover more time, orcompressed to “zoom in” on a shorter interval of time, as will be shownin more detail with respect to the exemplary physician interfacescreenshots of FIGS. 5-10.

Exemplary embodiments of an application operating on physician devices102 will now be described with reference to the screenshots depicted inFIGS. 5-10. It will be appreciated that the screenshots are onlyexemplary, and that any desired user interface, touch interface mobileapplication, user elements, or manipulatable icons or shapes may be usedto execute the method of FIG. 2.

FIG. 5 is a screenshot of a physician interface for logging into anapplication for reviewing patient ECG data. As discussed above, thelog-in interface of FIG. 5 may appropriately limit access to patientdata and comply with regulations, such as the Health InsurancePortability and Accountability Act (HIPAA).

FIG. 6 is another screenshot of a physician interface for reviewingpatient ECG data, according to an exemplary embodiment of the presentdisclosure. As shown in FIG. 6, patients may be classified into one of aplurality of patient groups based on a number, recency, and/or severityof detected arrhythmic events. For example, as shown in FIG. 6, patientgroups may be represented by indicia, in this case, a plurality ofindicia having different colors and sizes. In one embodiment, thepatient groups may include a first group for “review,” includingpatients that have experienced an arrhythmic event within a recent timethreshold, such as within the past month, week, or day, or since thephysician last reviewed the interface of FIG. 6. Patient groups mayinclude a second group for “continuing,” including patients that havenot experienced an arrhythmic event within a recent time threshold, suchas within the past month, week, or day, or since the physician lastreviewed the interface of FIG. 6. Patient groups may include a thirdgroup for “convert,” or “diagnosis,” including patients that havecompleted a prescribed monitoring period.

As shown in FIG. 6, in one embodiment, the physician interface mayinclude a “galaxy” interface 600 including a plurality of planetsfunctioning as indicia of each patient group, including a “review”planet 602 including three patients (represented as moons of the reviewplanet) that have experienced arrhythmic events within a specified timeperiod; a “convert or diagnose” planet 604 including six patients(represented as moons of the convert or diagnose planet) that havecompleted their prescribed monitoring period; and a “continuing” planet606 including 10 patients (represented as moons of the continuingplanet) that have not experienced arrhythmic events within a specifiedtime period. Also as shown in FIG. 6, the “review” patient group may berepresented by a relatively large and/or bright indicia, relative to asmaller and/or darker “convert or diagnose” patient group, and stillsmaller and/or darker “continuing” patient group. In one embodiment, allof the patients reflected in the plurality of patient groups of the“galaxy” interface of FIG. 6 may be patients of a single physician. Inone embodiment, a number or other indicia may be included on eachpatient indicia to indicate a number of recent or total arrhythmicevents experienced by the respective patient. For example, as shown inFIG. 6, the moon associated with each patient may have a number thatindicates the number of arrhythmic events experienced by the patientsince the patient began a monitoring period. Such a number could also oralternatively indicate a number of arrhythmic events experienced by thepatient since the physician reviewed the “galaxy” interface. Again, itwill be appreciated that the galaxy/planet/moon theme is only one ofmany suitable themes for indicia that change in color, size, and/orshape to indicate the identity and/or contents of a plurality of patientgroups, based on number, severity, and/or recency of detected arrhythmicevents.

In addition, indicia of each patient may reflect other attributes, suchas attributes of a patient device 104 associated with each patient. Forexample, patient indicia reflected in interface 600 may display one ormore of: a patient's compliance with device usage instructions; apatient's compliance with a prescribed treatment; a battery life of eachpatient's device 104; a transmitting speed and/or success of data sentfrom each device 104 to a network or server; and/or a connection levelbetween device 104 and a network, cellular tower, wireless access point,or other electronic device. In addition to or instead of showing anactual level, or amount of, power charge, connection, transmission,etc., the interface 600 may display a predetermined indicia when such avalue associated with the patient's device 104 has exceeded or droppedbelow a predetermined threshold. For example, indicia associated with apatient in interface 600 may become highlighted, bolded, outlined, orotherwise indicated as being different when, for example, a transmissionlevel has dropped, a connection has been interrupted, a signal has beenlost, a battery discharge level has been reached, etc. Thus, in additionto displaying a categorization of patients into different treatmenttypes (e.g., review, continuing, etc.), interface 600 may also quicklyand efficiently alert a physician when a user's patient device 104 isoperating sub-optimally. In one embodiment, indicia of a patientdisplayed in interface 600 may be marked with a red or green icon, e.g.,a light, to indicate a suboptimal battery level or signal level of apatent device 104. In one embodiment, a green light may indicate anacceptable battery level or signal level, whereas a red light mayindicate an undesirable battery level or signal level. In oneembodiment, a green light may indicate a patient's compliance withdevice usage instructions or a patient's compliance with a prescribedtreatment, whereas a red light may indicate a patient's lack ofcompliance with device usage instructions or a patient's lack ofcompliance with a prescribed treatment.

FIG. 7 is another screenshot of a physician interface for reviewingpatient ECG data, according to an exemplary embodiment of the presentdisclosure. FIG. 7 reflects that a physician may view a plurality ofinterfaces 702, 704, 706, 708 (e.g., separate “galaxy” interfaces asshown in FIG. 6), where each interface displays the patients of adifferent physician. Thus, in the “universe” view of FIG. 7, a physicianmay review the “galaxy” view of each physician in the physician'spractice, cohort, or other collaborative group. The view of FIG. 7provides a highly engaging, simple, and effective way for a physician toquickly identify patients of concern across a plurality of patientsunder the care of several physicians.

FIG. 8 is another screenshot of a physician interface for reviewingpatient ECG data, according to an exemplary embodiment of the presentdisclosure. Physician interface 800 of FIG. 8 may be configured todisplay ECG data and detected arrhythmic event indicia for a pluralityof patients. In addition, interface 800 may be configured to sort ECGdata and detected arrhythmic event indicia for the plurality of patientsbased on a classification of the patients into one or more of thepatient groups described above, including e.g. a “review” group,“convert or diagnose” group, and “continuing” group. In one embodiment,interface 800 may first display, at a top of the interface, the patientECG indicia 802 for patients in the “review” group because thosepatients have experienced an arrhythmic event within a threshold timeperiod, and are therefore of most concern. Interface 800 may nextdisplay, after patients in the “review” group, the patient ECG indicia804 for patients in the “convert or diagnose” group because thosepatients have completed their prescribed monitoring period, and shouldtherefore be diagnosed or converted to a different type of monitoringdevice or treatment. Interface 800 may finally display, at the bottom ofthe interface, the patient ECG indicia 806 for patients in the“continuing” group because those patients have not experienced anarrhythmic event within a threshold time period, and are therefore ofrelatively less concern.

As shown in FIG. 8, the ECG indicia for each patient may include aplurality of different combinations of data, indicia of data, and/orindicia of conditions, e.g., arrhythmic events. For example, in oneembodiment, each patient's ECG indicia may include a representation of araw, received ECG waveform, a heart rate trend line 801, and indicia ofany detected arrhythmic events displayed in relation to the ECG waveformat a position associated with a time of the detected arrhythmic event.In one embodiment, the indicia of each detected arrhythmic event maychange based on a severity, type, or recency of the detected arrhythmicevent. For example, as shown in FIG. 8, each arrhythmic event is showneither as a major event 810 (represented by a large, dark red circle), amoderate event 812 (represented by a medium red circle), or a minorevent (represented by a small grey circle). Of course, it will beappreciated that detected arrhythmic events may be represented by anysize, color, or shape of indicia, and that the size, color, or shape ofthe indicia may be changed in any desired way depending on any number ofparameters, such as severity, type, or recency of the detectedarrhythmic event. Thus, the interface 800 of FIG. 8 provides physicianswith a useful, effective, and engaging way to review numerous patientsunder the physician's care, where the patients with the most recentand/or severe detected arrhythmic events are displayed more prominentlythan other patients with less recent or severe detected arrhythmicevents.

In one embodiment, interface 800 may also display, for each patient, howlong the patient has worn, and/or been monitored, by a device 104. Forexample, interface 800 may display a number of days or weeks associatedwith each patient, reflecting the number of days or weeks the patienthas worn or been monitored by the device 104. In one embodiment,interface 800 may sort or categorize a display of patients based on thenumber of days or weeks the patient has worn or been monitored by thedevice 104.

In addition, interface 800 may indicate an activity level associatedwith each patient. For example, each patient device 104 may contain aGPS device, an accelerometer, and/or any other device that generateslocation, movement, or activity level data associated with a user.System 106 may process such received data to generate an activity levelto be associated with the patient. The activity level may be a range,(e.g., low, medium, high), a percentage of prescribed or maximumactivity, a numerical value associated with activity (e.g., a ranking ormoving average), or a time amount associated with the activity (e.g.,active for x of the past y hours). Interface 800 may then sort orcategorize a display of patients based on an activity level determinedfor each patient. Thus, a physician may easily view interface 800 todetermine relative or absolute activity levels of his or her patients toprovide a better understanding of their arrhythmic, cardiac, or evengeneral health status. In one embodiment, system 106 may generate alertsfor sending to physicians when a patient's activity level reaches acertain high or low threshold, and/or when a user has worn a monitoringdevice for some predetermined amount of time.

In addition, interface 800 may indicate whether a patient is complyingwith a prescribed medication, treatment, activity, or other regimen. Forexample, system 106 may track each patient's compliance with aprescribed regimen, e.g., through accelerometers, blood glucose sensors,or any other biocompatible sensors. System 106 may then determinewhether a patient is complying with a physician-prescribed regimen, andif desired, generate one or more alerts for sending to a physician whena patient is not in compliance with his or her prescribed regimen. Forexample, system 106 may alert a physician when a user is not following aprescribed drug treatment program, diet program and/or exercise program.It should be appreciated that the above-discussed indicia and relatedfunctionality (e.g., medical device battery level/signal, patient devicemonitoring period, patient activity level, patient compliance, etc.) maybe incorporated into any of the physician interfaces described in thepresent disclosure.

FIG. 9 is another screenshot of a physician interface for reviewingpatient ECG data, according to an exemplary embodiment of the presentdisclosure. Specifically, FIG. 9 depicts an interface 900 which providesa somewhat more detailed view of a physician interface for reviewing apatient's ECG data 901, while still also providing a limited view of ECGdata 922 for other patients under the physician's care. In oneembodiment, the ECG data 922 for other patients may resemble the ECGdata displayed in the overview interface 800 of FIG. 8, while a moredetailed ECG data 901 is displayed for the selected patient. Thephysician may request and therefore receive the interface view of FIG. 9by tapping or otherwise selecting one of the patients in the interfaceview of FIG. 8 (e.g., the physician tapped or selected “Eric Benoit” inthe view of FIG. 8 to obtain the interface view of FIG. 9). Thephysician may switch from a detailed view of one patient to a detailedview of another patient by tapping, swiping, or otherwise selecting oneof the other patients 922 displayed in interface 900.

As shown in FIG. 9, in the detailed view of interface 900, additionalECG or other health data may be displayed for the selected patient,including a categorized list of arrhythmic events 902 (e.g., “SVT”[supraventricular tachycardia], “VT” [ventricular tachycardia],“Pauses,” and “Bradycardia”), and heart rate parameters 904 (e.g., beatsper minute (“bpm”), average bpm, and maximum bpm). Also, as in theinterface 800, interface 900 may display the ECG waveform, heart ratetrendline 914, and indicia of detected arrhythmic events in relation tothe ECG waveform at a position associated with a time of the detectedarrhythmic event. The indicia of detected arrhythmic events may includeminor event indicia 908, moderate event indicia 910, and major eventindicia 912. In addition to what is displayed in interface 800, the moredetailed interface 900 may also display for the selected patient anextended ECG waveform 918, which may be a “zoomed-in” display of asubset selection 920 of an even more extended duration ECG waveform 916.The physician may then slide subset selection 920, as defined, e.g., bya shaded portion or bracket along extended duration ECG waveform 916 tochange the displayed portion of zoomed-in waveform 918. The physicianmay also use various input methods to expand or compress the subsetselection 920, such as by squeezing or pinching a touchscreen interfaceof the physician device 102. In one embodiment, various portions of theextended waveform 918 or waveform 916 may be highlighted, darkened,bolded, or otherwise indicated as being associated with an arrhythmicevent. Accordingly, the interface 900 may prompt a physician toinvestigate and review various heart parameters, statistics, and ECGdata associated with events of significant import.

FIG. 10 is another screenshot of a physician interface for reviewingpatient ECG data, according to an exemplary embodiment of the presentdisclosure. Specifically, FIG. 10 depicts an even more detailedinterface 1000 of a specific patient under review. Interface 1000 mayagain include arrhythmic events 1002 (e.g., “SVT,” “VT,” “Pauses,” and“Bradycardia”), and heart rate parameters (e.g., beats per minute(“bpm”), average bpm, and maximum bpm), but also patient-triggeredcomplaints 1004 (e.g., “chest discomfort,” “palpitations,” “dizziness,”etc.) associated with each arrhythmic event. In addition, detailedinterface 1000 may also categorize detected arrhythmic events intoday/night events 1006, based on the number and/or duration of theevents, to assist a physician in associating events with certain day ornight activities, and for facilitating proposed treatments orinterventions. As in the interface 900 of FIG. 9, interface 100 may alsoinclude a heartrate trendline 1008, and an extended ECG waveform 1010defined by subset selection 1012 of further extended ECG waveform 1014.Also, as in interface 900, a physician may interact with a touchscreenof physician device 102 to manipulate subset selection 1012 to identify,investigate and review various heart parameters, statistics, and ECGdata associated with arrhythmic events of significant import.

In one embodiment, in addition to identifying arrhythmic events, asdescribed above, a physician may add to a patient's ECG data any otherevent or timeframe of interest to the physician or relevance to thepatient's health. For example, a physician may associate with apatient's ECG data the date or time at which an ablation treatmentprocedure was performed on a patient. Thus, the physician may define atimeframe preceding the indicated ablation procedure as containingpre-ablation ECG data, and a timeframe following the indicated ablationprocedure as containing post-ablation ECG data. Any of the interfacesdisclosed herein may then enable a physician to view, analyze, sort, andcompare patient ECG data in relation to pre- and post-ablation timeperiods. Physicians may perform similar techniques for any othertreatment procedure relevant to patient cardiac health.

Categorizing and sorting ECG data relative to a timeframe of a medicalprocedure may assist physicians in evaluating the effectiveness of themedical procedure. For example, a physician may generate a report,and/or display on opposing sides of a display device, ECG data for timeperiods preceding and following a medical procedure, such as an ablationprocedure for treating arrhythmia. A physician may then more easilycompare ECG data and arrhythmic events between different time frames.Relevant time frames may also be segmented, cross-referenced, ornormalized by, e.g., time-of-day, activity level, blood pressure, etc.For example, a physician may compare a patient's morning, pre-ablativeECG data to the patient's morning, post-ablative ECG data.Alternatively, a physician may compare a patient's pre-ablative,post-exercise ECG data to the patient's post-ablative, post-exercise ECGdata. It will be appreciated that any combination of timeframes, basedon treatment periods or events, heart rate periods or events, or anyother timeframe of interest may be used to generate a display of ECGdata for a physician to view through any of the presently-disclosedinterfaces.

Although embodiments of the present invention have been described asdetecting and verifying suspected arrhythmias, other embodiments may besimilarly configured and used to detect and verify other health orfitness conditions, such as inappropriate insulin level, respiration,blood pressure, SpO2, body movement, exertion and the like.

A remote health monitoring system may include a processor controlled byinstructions stored in a memory. For example, the transceiver assemblymay include and be controlled by such a processor, and the remote servermay be controlled by another such processor. The memory may be randomaccess memory (RAM), read-only memory (ROM), flash memory or any othermemory, or combination thereof, suitable for storing control software orother instructions and data.

Some of the functions performed by the remote health monitoring systemhave been described with reference to flowcharts and/or block diagrams.Those skilled in the art should readily appreciate that functions,operations, decisions, etc. of all or a portion of each block, or acombination of blocks, of the flowcharts or block diagrams may beimplemented as computer program instructions, software, hardware,firmware or combinations thereof.

Those skilled in the art should also readily appreciate thatinstructions or programs defining the functions of the present inventionmay be delivered to a processor in many forms, including, but notlimited to, information permanently stored on non-writable storage media(e.g. read-only memory devices within a computer, such as ROM, ordevices readable by a computer I/O attachment, such as CD-ROM or DVDdisks), information alterably stored on writable storage media (e.g.floppy disks, removable flash memory and hard drives) or informationconveyed to a computer through communication media, including wired orwireless computer network.

In addition, while the invention may be embodied in software, thefunctions necessary to implement the invention may optionally oralternatively be embodied in part or in whole using firmware and/orhardware components, such as combinatorial logic, Application SpecificIntegrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs) orother hardware or some combination of hardware, software and/or firmwarecomponents.

Other embodiments of the disclosure will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1-29. (canceled)
 30. A method for displaying representations of a groupon a screen of an electronic device, the method comprising: classifyingdata into a plurality of groups; generating a first visualrepresentation for one group of the plurality of groups, wherein thefirst visual representation includes a shape having a periphery;generating a plurality of second visual representations, each of theplurality of second visual representations corresponding to datarelating to a respective member of the one group; and displaying thefirst visual representation and the plurality of second visualrepresentations on the screen, wherein the plurality of second visualrepresentations are positioned about the shape and outside of theperiphery.
 31. The method of claim 30, wherein the data includes datarelating to a heart of a patient.
 32. The method of claim 30, whereinthe data includes patient ECG data.
 33. The method of claim 32, furthercomprising analyzing the patient ECG data to detect arrhythmic events.34. The method of claim 33, wherein classifying the data is based on oneof a severity and a recency of detected arrhythmic events.
 35. Themethod of claim 30, wherein generating a first visual representation forone group of the plurality of groups includes generating a plurality offirst visual representations, each of the plurality of first visualrepresentations corresponding to a respective group of the plurality ofgroups.
 36. The method of claim 35, wherein each of the plurality offirst visual representations includes a unique size, shape, or color.37. The method of claim 35, wherein each of the plurality of firstvisual representations is displayed on the screen at a position spacedfrom another first visual representation.
 38. The method of claim 33,wherein the plurality of groups includes a first group includingpatients that have experienced an arrhythmic event within a first timeperiod, a second group including patients that have not experienced anarrhythmic event within a second time period, and a third groupincluding patients that have completed a monitoring period.
 39. Themethod of claim 30, wherein the first visual representation includes anindication relating to members in the one group.
 40. The method of claim39, wherein the indication includes a numeral corresponding to a numberof members in the one group.
 41. The method of claim 33, wherein each ofthe plurality of second visualizations includes an indication relatingto arrhythmic events experienced by the respective member of the onegroup.
 42. The method of claim 41, wherein the indication includes anumeral corresponding to a number of arrhythmic events experienced bythe respective member.
 43. The method of claim 30, further comprising:generating and displaying indicia relating to a status of a datacollection device.
 44. The method of claim 43, wherein the indicia isconfigured to change a configuration if the status of the datacollection device changes.
 45. A system for displaying representationsof a group, the system comprising: a data storage device storinginstructions for displaying the representations; a processor configuredto execute the instructions to perform a method comprising: classifyingdata into a plurality of groups; generating a first visualrepresentation for one group of the plurality of groups, wherein thefirst visual representation includes a shape having a periphery;generating a plurality of second visual representations, each of theplurality of second visual representations corresponding to datarelating to a respective member of the one group; and a display deviceconfigured to display the first visual representation and the pluralityof second visual representations such that the plurality of secondvisual representations are positioned about the shape and outside of theperiphery.
 46. The system of claim 45, wherein the data includes patientECG data.
 47. The system of claim 46, further comprising analyzing thepatient ECG data to detect arrhythmic events; and wherein classifyingthe data into a plurality groups includes sorting the detectedarrhythmic events based on one of a severity and a recency.
 48. Thesystem of claim 45, wherein generating a first visual representation forone group of the plurality of groups includes generating a plurality offirst visual representations, each of the plurality of first visualrepresentations corresponding to a respective group of the plurality ofgroups, and wherein each of the plurality of first visualrepresentations includes a unique size, shape, or color.
 49. The systemof claim 47, wherein the plurality of groups includes a first groupincluding patients that have experienced an arrhythmic event within afirst time period, a second group including patients that have notexperienced an arrhythmic event within a second time period, and a thirdgroup including patients that have completed a monitoring period. 50.The method of claim 45, wherein the first visual representation includesa numeral disposed within the periphery of the shape, the numeralcorresponding to a number of members in the one group.
 51. The method ofclaim 47, wherein each of the plurality of second visual representationsincludes a numeral corresponding to a number of arrhythmic eventsexperienced by the respective member.
 52. The method of claim 45,wherein the processor is further configured to: generate and displayindicia relating to a status of a data collection device, wherein theindicia is configured to change a configuration if the status of thedata collection device changes.
 53. A handheld device for displayingrepresentations of a group, the handheld device comprising: a memorydevice storing instructions for displaying the representations on thehandheld device; a processor configured to execute instructions for:generating a plurality of first visual representations corresponding toa plurality of groups based on classification of data, wherein each ofthe plurality of first visual representations includes a shape having aperiphery; for each of the plurality of first visual representations,generating a plurality of second visual representations, each of theplurality of second visual representations corresponding to datarelating to a respective member of a respective group of data; and adisplay unit configured to display the first visual representation andthe plurality of second visual representations such that the pluralityof second visual representations are positioned about the shape andoutside of the periphery.
 54. The handheld device of claim 53, whereinthe data includes patient ECG data, the processor is further configuredto analyzing the patient ECG data to detect arrhythmic events, andclassification of data includes sorting the detected arrhythmic eventsbased on one of a severity and a recency.
 55. The handheld device ofclaim 54, wherein each of the plurality of first visual representationsincludes a unique size, shape, or color.
 56. The handheld device ofclaim 54, wherein the plurality of groups includes a first groupincluding patients that have experienced an arrhythmic event within afirst time period, a second group including patients that have notexperienced an arrhythmic event within a second time period, and a thirdgroup including patients that have completed a monitoring period. 57.The handheld device of claim 53, wherein each first visualrepresentation includes a numeral disposed within the periphery of theshape, the numeral corresponding to a number of members in thecorresponding group.
 58. The handheld device of claim 54, wherein eachof the plurality of second visualizations includes a numeralcorresponding to a number of arrhythmic events experienced by therespective member.
 59. The method of claim 53, wherein the processor isfurther configured to: generate and display indicia relating to a statusof a data collection device, wherein the indicia is configured to changea configuration if the status of the data collection device changes.